Ryan Edge

I built the same four-screen app six ways — native, React Native, Flutter, Compose Multiplatform — with agents doing the work. Some teams dismiss native's cost too quickly. In my tests, it was the slowest by a wide margin — up to 3.5× the agent minutes.

Visual fidelity to the design spec: Flutter came in first, Compose Multiplatform was a close second, React Native was third, and both native builds last. The two native apps failed the same way — off-design buttons and tabs, an extra border on every card. On one run.

I built the same four-screen app six ways — native, React Native, Flutter, Compose Multiplatform — with agents doing the work. Some teams dismiss native's cost too quickly. In my tests, it was the slowest by a wide margin — up to 3.5× the agent minutes.

"Native is cheaper now" is the wrong takeaway. What speeds an agent up is checking its own work in milliseconds instead of booting a simulator for every change. That's an architecture choice, not a language choice — and you can make it in Flutter or CMP too.

I built the same four-screen app six ways — native, React Native, Flutter, Compose Multiplatform — with agents doing the work. Some teams dismiss native's cost too quickly. In my tests, it was the slowest by a wide margin — up to 3.5× the agent minutes.

Which makes me think "who writes the code" was never the interesting variable. Multiple codebases equal multiple chances to be wrong. That equals higher maintenance costs.

Shopify is going back to native from React Native. But they changed two things at once: the language, and a new agent-addressable architecture. Two-variable experiment, one-variable conclusion — the headline will read "native won," and the evidence doesn't isolate that.

Every Helix checkpoint needs a human nod + two adversarial reviews. Agents scale; review attention doesn't, so doubling codebases doubles review load. By how much per feature? Three numbers: headless vs simulator cycle time, review-minutes/feature, first-pass rate by language.

Shopify is going back to native from React Native. But they changed two things at once: the language, and a new agent-addressable architecture. Two-variable experiment, one-variable conclusion — the headline will read "native won," and the evidence doesn't isolate that.

The 12-week Shop rebuild has the same problem. Helix reads the RN codebase to plan checkpoints. The RN app is the spec. So the number measures how cheap rewrites got, not how cheap building two apps from ambiguous requirements got.

Shopify is going back to native from React Native. But they changed two things at once: the language, and a new agent-addressable architecture. Two-variable experiment, one-variable conclusion — the headline will read "native won," and the evidence doesn't isolate that.

Good reads from June

Jun 25 — The Cost YAGNI Was Never About newsletter.kentbeck.com/p/the-cost-... "You aren't gonna need it" was never about saving typing effort, even though free AI-generated code makes that reading tempting. It's about two costs: locking into a guessed design early, paying before you must.

Good reads from June

Jun 16 — The New Software Lifecycle addyosmani.com/blog/new-sd... A Google white paper argues most AI coding agent failures trace to their setup, not the model underneath. Writing code is basically solved, it says; the bottleneck shifted to clear specs and verifying what got built.

Good reads from June

Jun 11 — Why AI Hasn't Replaced Software Engineers and Won't www.normaltech.ai/p/why-ai-ha... Digging into major layoffs blamed on AI, this finds cost-cutting behind most, not AI. Its argument: writing code was never the bottleneck — deciding what to build and owning what ships are.

Good reads from June

Jun 7 — A New Era for Software Testing antirez.com/news/168 AI-written code now beats typical hand-written code, even if it still lags the best. The bigger idea: give an AI agent a checklist and let it act as your QA tester, running checks you'd otherwise skip.

Good reads from June

For a while, I've felt about 20% more productive with AI, so DX's findings sound accurate. leaddev.com/reporting/a...

AI gave product engineering teams a raise: same effort, more output. The trap is spending it all — more scope, more surface area — instead of investing the surplus.

Code is cheaper to write now, but ownership is still full price: maintenance, reviews, on-call, and “wait, how does this work again?”